Shorter, but Still Trustworthy? An Empirical Study of Chain-of-Thought Compression

• URL: http://arxiv.org/abs/2604.04120v1
• Date: Sun, 05 Apr 2026 13:43:12 GMT
• Title: Shorter, but Still Trustworthy? An Empirical Study of Chain-of-Thought Compression
• Abstract summary: Long chain-of-thought (Long-CoT) reasoning models have motivated a growing body of work on compressing reasoning traces to reduce inference cost.<n>We conduct the first systematic empirical study of how CoT compression affects model trustworthiness.<n>We find that CoT compression frequently introduces trustworthiness regressions and that different methods exhibit markedly different degradation profiles across dimensions.
• Score: 19.669117846064562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long chain-of-thought (Long-CoT) reasoning models have motivated a growing body of work on compressing reasoning traces to reduce inference cost, yet existing evaluations focus almost exclusively on task accuracy and token savings. Trustworthiness properties, whether acquired or reinforced through post-training, are encoded in the same parameter space that compression modifies. This means preserving accuracy does not, a priori, guarantee preserving trustworthiness. We conduct the first systematic empirical study of how CoT compression affects model trustworthiness, evaluating multiple models of different scales along three dimensions: safety, hallucination resistance, and multilingual robustness. Under controlled comparisons, we find that CoT compression frequently introduces trustworthiness regressions and that different methods exhibit markedly different degradation profiles across dimensions. To enable fair comparison across bases, we propose a normalized efficiency score for each dimension that reveals how naïve scalar metrics can obscure trustworthiness trade-offs. As an existence proof, we further introduce an alignment-aware DPO variant that reduces CoT length by 19.3\% on reasoning benchmarks with substantially smaller trustworthiness loss. Our findings suggest that CoT compression should be optimized not only for efficiency but also for trustworthiness, treating both as equally important design constraints.

Related papers

• Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction [21.411353047208777]
  We benchmark 12 large language models (LLMs) under various compression configurations across five NLP tasks.<n>Our experiments reveal that: (I) compression frequently decouples accuracy from uncertainty; (II) larger models absorb compression-induced uncertainty far more effectively than smaller ones; and (III) uncertainty inflation is often threshold-like rather than gradual.
  arXiv  Detail & Related papers  (2026-06-01T08:00:17Z)
• Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains [9.979461886634022]
  Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models.<n>Existing latent reasoning methods treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compressed.<n>We propose Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into latent representations.
  arXiv  Detail & Related papers  (2026-05-25T11:57:09Z)
• Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression [55.63153956934198]
  Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs)<n>Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios.<n>We propose a novel EXTreme-RAtio Chain-of-Thought Compression framework, termed Extra-CoT, which aggressively reduces the token budget while preserving answer accuracy.
  arXiv  Detail & Related papers  (2026-02-09T06:57:15Z)
• Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency [78.91846841708586]
  We show that even facts answered with perfect self-consistency can rapidly collapse under mild contextual interference.<n>We propose Neighbor-Consistency Belief (NCB), a structural measure of belief that evaluates response coherence across a conceptual neighborhood.<n>We also present Structure-Aware Training (SAT), which optimize context-invariant belief structure and reduces long-tail knowledge brittleness by approximately 30%.
  arXiv  Detail & Related papers  (2026-01-09T16:23:21Z)
• Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs? [32.02698064940949]
  Chain-of-thought (CoT) prompting has emerged as a common technique for enhancing the reasoning abilities of large language models.<n>We present the first systematic study of training-free confidence estimation methods for CoT gating.
  arXiv  Detail & Related papers  (2025-10-23T21:33:28Z)
• Downsized and Compromised?: Assessing the Faithfulness of Model Compression [0.0]
  This paper presents a novel approach to evaluating faithfulness in compressed models, moving beyond standard metrics.<n>We introduce and demonstrate a set of faithfulness metrics that capture how model behavior changes post-compression.<n>Our contributions include introducing techniques to assess predictive consistency between the original and compressed models using model agreement, and applying chi-squared tests to detect statistically significant changes in predictive patterns across both the overall dataset and demographic subgroups.
  arXiv  Detail & Related papers  (2025-10-07T17:05:02Z)
• ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning [64.93140713419561]
  Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs.<n>Existing fine-tuning-based compression methods either operate post-hoc pruning, risking disruption to reasoning coherence, or rely on sampling-based selection.<n>We introduce ConCISE, a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, and Early Stopping to terminate reasoning when confidence is sufficient.
  arXiv  Detail & Related papers  (2025-05-08T01:40:40Z)
• UNComp: Can Matrix Entropy Uncover Sparsity? -- A Compressor Design from an Uncertainty-Aware Perspective [85.08718140718707]
  UNComp is an uncertainty-aware framework that uncovers sparsity patterns that can be used for adaptive compression.<n>By focusing on uncertainty to analyze the sparsity pattern in detail, UNComp reduces the KV cache size to 4.74% of the original, achieves a 6% prefill speedup, and improves throughput by 6.4x.
  arXiv  Detail & Related papers  (2024-10-04T02:32:36Z)
• Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression [109.23761449840222]
  This study conducts the first, thorough evaluation of leading Large Language Models (LLMs) We find that quantization is currently a more effective approach than pruning in achieving efficiency and trustworthiness simultaneously.
  arXiv  Detail & Related papers  (2024-03-18T01:38:19Z)
• Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression [49.288196234823005]
  We propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model mimics the original model. We also explore the effect of compression with regard to robustness under adversarial attacks.
  arXiv  Detail & Related papers  (2021-09-07T17:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.